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نتیجه جستجو - architecture

تعداد مقالات یافته شده: 847
ردیف عنوان نوع
1 IoT architecture for continuous long term monitoring: Parkinson’s Disease case study
معماری اینترنت اشیا برای نظارت طولانی مدت مداوم: مطالعه موردی بیماری پارکینسون-2022
In recent years, technological advancements and the strengthening of the Internet of Things concepts have led to significant improvements in the technology infrastructures for remote monitoring. This includes telemedicine which is the ensemble of technologies and tools involved in medical services, from consultations, to diagnosis, prescriptions, treatment and patient monitoring, all done remotely via an Internet connection.
Developing a telemedicine framework capable of monitoring patients over a continuous long-term monitoring window may encounter various issues related to the battery life of the device or the accuracy of the retrieved data. Moreover, it is crucial to develop an IoT architecture that is adaptable to various scenarios and the ongoing changes of the application scenario under analysis.
In this work, we present an IoT architecture for continuous long-term monitoring of patients. Furthermore, as a real scenario case study, we adapt our IoT architecture for Parkinson’s Disease management, building up the PDRMA (Parkinson’s disease remote monitoring architecture). Performance analysis for optimal operation with respect to temperature and daily battery life is conducted. Finally, a multi-parameter app for the continuous monitoring of Parkinson’s patients is presented.
keywords: IoT | Telemedicine | Continuous long term monitoring | Parkinson’s disease | e-Health
مقاله انگلیسی
2 DQRA: Deep Quantum Routing Agent for Entanglement Routing in Quantum Networks
DQRA: عامل مسیریابی کوانتومی عمیق برای مسیریابی درهم تنیده در شبکه های کوانتومی-2022
Quantum routing plays a key role in the development of the next-generation network system. In particular, an entangled routing path can be constructed with the help of quantum entanglement and swapping among particles (e.g., photons) associated with nodes in the network. From another side of computing, machine learning has achieved numerous breakthrough successes in various application domains, including networking. Despite its advantages and capabilities, machine learning is not as much utilized in quantum networking as in other areas. To bridge this gap, in this article, we propose a novel quantum routing model for quantum networks that employs machine learning architectures to construct the routing path for the maximum number of demands (source–destination pairs) within a time window. Specifically, we present a deep reinforcement routing scheme that is called Deep Quantum Routing Agent (DQRA). In short, DQRA utilizes an empirically designed deep neural network that observes the current network states to accommodate the network’s demands, which are then connected by a qubit-preserved shortest path algorithm. The training process of DQRA is guided by a reward function that aims toward maximizing the number of accommodated requests in each routing window. Our experiment study shows that, on average, DQRA is able to maintain a rate of successfully routed requests at above 80% in a qubit-limited grid network and approximately 60% in extreme conditions, i.e., each node can be repeater exactly once in a window. Furthermore, we show that the model complexity and the computational time of DQRA are polynomial in terms of the sizes of the quantum networks.
INDEX TERMS: Deep learning | deep reinforcement learning (DRL) | machine learning | next-generation network | quantum network routing | quantum networks.
مقاله انگلیسی
3 Moving towards intelligent telemedicine: Computer vision measurement of human movement
حرکت به سمت پزشکی از راه دور هوشمند: اندازه گیری بینایی کامپیوتری حرکت انسان-2022
Background: Telemedicine video consultations are rapidly increasing globally, accelerated by the COVID- 19 pandemic. This presents opportunities to use computer vision technologies to augment clinician visual judgement because video cameras are so ubiquitous in personal devices and new techniques, such as DeepLabCut (DLC) can precisely measure human movement from smartphone videos. However, the accuracy of DLC to track human movements in videos obtained from laptop cameras, which have a much lower FPS, has never been investigated; this is a critical gap because patients use laptops for most telemedicine consultations. Objectives: To determine the validity and reliability of DLC applied to laptop videos to measure finger tapping, a validated test of human movement. Method: Sixteen adults completed finger-tapping tests at 0.5 Hz, 1 Hz, 2 Hz, 3 Hz and at maximal speed. Hand movements were recorded simultaneously by a laptop camera at 30 frames per second (FPS) and by Optotrak, a 3D motion analysis system at 250 FPS. Eight DLC neural network architectures (ResNet50, ResNet101, ResNet152, MobileNetV1, MobileNetV2, EfficientNetB0, EfficientNetB3, EfficientNetB6) were applied to the laptop video and extracted movement features were compared to the ground truth Optotrak motion tracking. Results: Over 96% (529/552) of DLC measures were within +∕−0.5 Hz of the Optotrak measures. At tapping frequencies >4 Hz, there was progressive decline in accuracy, attributed to motion blur associated with the laptop camera’s low FPS. Computer vision methods hold potential for moving us towards intelligent telemedicine by providing human movement analysis during consultations. However, further developments are required to accurately measure the fastest movements.
keywords: پزشکی از راه دور | ضربه زدن با انگشت | موتور کنترل | کامپیوتری | Telemedicine | DeepLabCut | Finger tapping | Motor control | Computer vision
مقاله انگلیسی
4 Efficient Hardware Implementation of Finite Field Arithmetic AB + C for Binary Ring-LWE Based Post-Quantum Cryptography
اجرای سخت افزار کارآمد محاسبات میدان محدود AB + C برای رمزنگاری پس کوانتومی مبتنی بر حلقه باینری-LWE-2022
Post-quantum cryptography (PQC) has gained significant attention from the community recently as it is proven that the existing public-key cryptosystems are vulnerable to the attacks launched from the well-developed quantum computers. The finite field arithmetic AB þ C, where A and C are integer polynomials and B is a binary polynomial, is the key component for the binary Ring-learning-with-errors (BRLWE)- based encryption scheme (a low-complexity PQC suitable for emerging lightweight applications). In this paper, we propose a novel hardware implementation of the finite field arithmetic AB þ C through three stages of interdependent efforts: (i) a rigorous mathematical formulation process is presented first; (ii) an efficient hardware architecture is then presented with detailed description; (iii) a thorough implementation has also been given along with the comparison. Overall, (i) the proposed basic structure (u ¼ 1) outperforms the existing designs, e.g., it involves 55.9% less area-delay product (ADP) than [13] for n ¼ 512; (ii) the proposed design also offers very efficient performance in time-complexity and can be used in many future applications.
INDEX TERMS: Binary ring-learning-with-errors | finite field arithmetic | FPGA platform | hardware design | post-quantum cryptography
مقاله انگلیسی
5 Deep convolutional neural networks-based Hardware–Software on-chip system for computer vision application
سیستم سخت‌افزار-نرم‌افزار روی تراشه مبتنی بر شبکه‌های عصبی عمیق برای کاربرد بینایی ماشین-2022
Embedded vision systems are the best solutions for high-performance and lightning-fast inspection tasks. As everyday life evolves, it becomes almost imperative to harness artificial intelligence (AI) in vision applications that make these systems intelligent and able to make decisions close to or similar to humans. In this context, the AI’s integration on embedded systems poses many challenges, given that its performance depends on data volume and quality they assimilate to learn and improve. This returns to the energy consumption and cost constraints of the FPGA-SoC that have limited processing, memory, and communication capacity. Despite this, the AI algorithm implementation on embedded systems can drastically reduce energy consumption and processing times, while reducing the costs and risks associated with data transmission. Therefore, its efficiency and reliability always depend on the designed prototypes. Within this range, this work proposes two different designs for the Traffic Sign Recognition (TSR) application based on the convolutional neural network (CNN) model, followed by three implantations on PYNQ-Z1. Firstly, we propose to implement the CNN-based TSR application on the PYNQ-Z1 processor. Considering its runtime result of around 3.55 s, there is room for improvement using programmable logic (PL) and processing system (PS) in a hybrid architecture. Therefore, we propose a streaming architecture, in which the CNN layers will be accelerated to provide a hardware accelerator for each layer where direct memory access (DMA) interface is used. Thus, we noticed efficient power consumption, decreased hardware cost, and execution time optimization of 2.13 s, but, there was still room for design optimizations. Finally, we propose a second co-design, in which the CNN will be accelerated to be a single computation engine where BRAM interface is used. The implementation results prove that our proposed embedded TSR design achieves the best performances compared to the first proposed architectures, in terms of execution time of about 0.03 s, computation roof of about 36.6 GFLOPS, and bandwidth roof of about 3.2 GByte/s.
keywords: CNN | FPGA | Acceleration | Co-design | PYNQ-Z1
مقاله انگلیسی
6 Image2Triplets: A computer vision-based explicit relationship extraction framework for updating construction activity knowledge graphs
Image2Triplets: چارچوب استخراج رابطه صریح مبتنی بر بینایی ماشین برای به روز رسانی نمودارهای دانش فعالیت های ساخت-2022
Knowledge graph (KG) is an effective tool for knowledge management, particularly in the architecture, engineering and construction (AEC) industry, where knowledge is fragmented and complicated. However, research on KG updates in the industry is scarce, with most current research focusing on text-based KG updates. Considering the superiority of visual data over textual data in terms of accuracy and timeliness, the potential of computer vision technology for explicit relationship extraction in KG updates is yet to be ex- plored. This paper combines zero-shot human-object interaction detection techniques with general KGs to propose a novel framework called Image2Triplets that can extract explicit visual relationships from images to update the construction activity KG. Comprehensive experiments on the images of architectural dec- oration processes have been performed to validate the proposed framework. The results and insights will contribute new knowledge and evidence to human-object interaction detection, KG update and construc- tion informatics from the theoretical perspective. © 2022 Elsevier B.V. All rights reserved.
keywords: یادگیری شات صفر | تشخیص تعامل انسان و شی | بینایی ماشین| استخراج رابطه صریح | نمودار دانش | Zero-shot learning | Human-object interaction detection | Computer vision | Explicit relationship extraction | Knowledge graph
مقاله انگلیسی
7 Epsilon-Nets, Unitary Designs, and Random Quantum Circuits
شبکه های اپسیلون، طرح های واحد و مدارهای کوانتومی تصادفی-2022
Epsilon-nets and approximate unitary t-designs are natural notions that capture properties of unitary operations relevant for numerous applications in quantum information and quantum computing. In this work we study quantitative connections between these two notions. Specifically, we prove that, for d dimensional Hilbert space, unitaries constituting δ-approximate t-expanders form -nets for t d5/2 and δ 3d/2 d2. We also show that for arbitrary t, -nets can be used to construct δ-approximate unitary t-designs for δ t, where the notion of approximation is based on the diamond norm. Finally, we prove that the degree of an exact unitary t design necessary to obtain an -net must grow at least as fast as 1 (for fixed dimension) and not slower than d2 (for fixed ). This shows near optimality of our result connecting t-designs and nets. We apply our findings in the context of quantum computing. First, we show that that approximate t-designs can be generated by shallow random circuits formed from a set of universal twoqudit gates in the parallel and sequential local architectures considered in (Brandão et al., 2016). Importantly, our gate sets need not to be symmetric (i.e., contains gates together with their inverses) or consist of gates with algebraic entries. Second, we consider compilation of quantum gates and prove a nonconstructive Solovay-Kitaev theorem for general universal gate sets. Our main technical contribution is a new construction of efficient polynomial approximations to the Dirac delta in the space of quantum channels, which can be of independent interest.]
Index Terms: Unitary designs, epsilon nets | random quantum circuits | compilation of quantum gates | unitary channels.
مقاله انگلیسی
8 Computer vision for anatomical analysis of equipment in civil infrastructure projects: Theorizing the development of regression-based deep neural networks
چشم انداز کامپیوتری برای تجزیه و تحلیل آناتومیکی تجهیزات در پروژه های زیرساختی عمرانی: نظریه پردازی توسعه شبکه های عصبی عمیق مبتنی بر رگرسیون-2022
There is high demand for heavy equipment in civil infrastructure projects and their performance is a determinant of the successful delivery of site operations. Although manufacturers provide equipment performance hand- books, additional monitoring mechanisms are required to depart from measuring performance on the sole basis of unit cost for moved materials. Vision-based tracking and pose estimation can facilitate site performance monitoring. This research develops several regression-based deep neural networks (DNNs) to monitor equipment with the aim of ensuring safety, productivity, sustainability and quality of equipment operations. Annotated image libraries are used to train and test several backbone architectures. Experimental results reveal the pre- cision of DNNs with depthwise separable convolutions and computational efficiency of DNNs with channel shuffle. This research provides scientific utility by developing a method for equipment pose estimation with the ability to detect anatomical angles and critical keypoints. The practical utility of this study is the provision of potentials to influence current practice of articulated machinery monitoring in projects.
keywords: هوش مصنوعی (AI) | سیستم های فیزیکی سایبری | معیارهای ارزیابی خطا | طراحی و آزمایش تجربی | تخمین ژست کامل بدن | صنعت و ساخت 4.0 | الگوریتم های یادگیری ماشین | معماری های ستون فقرات شبکه | Artificial intelligence (AI) | Cyber physical systems | Error evaluation metrics | Experimental design and testing | Full body pose estimation | Industry and construction 4.0 | Machine learning algorithms | Network backbone architectures
مقاله انگلیسی
9 Mapping Nearest Neighbor Compliant Quantum Circuits Onto a 2-D Hexagonal Architecture
نگاشت مدارهای کوانتومی منطبق با نزدیکترین همسایه بر روی یک معماری دو بعدی شش ضلعی-2022
Quantum algorithms can be described as quantum circuits and are supposed to be carried out on an ideal quantum device that is far from current ones. The current quantum devices have a significant limitation on the connectivity between quantum bits. In other words, a quantum bit is only allowed to interact with its nearest neighbors (NNs). In reality, quantum bits have to be placed on a grid, where the connectivity between quantum bits is predefined. The predefined connectivity of a grid further determines the possible range of architectures of a quantum device after the placement of quantum bits. In this article, we propose to place quantum bits based on a 2-D hexagonal architecture rather than a 2-D Cartesian architecture. To validate the effectiveness, we leverage a workflow for mapping NN compliant quantum circuits onto targeting grids, where the workflow consists of a global reordering strategy and a local reordering strategy. With the advantages of the hexagonal grid, the overhead of making quantum circuits NN compliant is reduced significantly compared with the Cartesian grid. We also provide a comprehensive set of ablation analyses to gain a better understanding of the contributions of the components within our workflow. According to the experimental results, when changing the grid type from Cartesian to hexagonal, the global reordering strategy is crucial for small quantum circuits. In contrast, the local reordering strategy is more important than the global reordering strategy for large quantum circuits.
Index Terms: 2-D architecture | hexagonal grid | nearest neighbor (NN) compliant | quantum circuit.
مقاله انگلیسی
10 GAFL: Global adaptive filtering layer for computer vision
GAFL: لایه فیلتر تطبیقی جهانی برای بینایی کامپیوتر-2022
We devise a universal global adaptive filtering layer, GAFL, capable of ‘‘learning’’ optimal frequency filter for each image in a dataset together with the weights of the base neural network that performs some computer vision task. The proposed approach takes the source image in the spatial domain, selects the best frequencies in the Fourier domain for the benefit of the global task, and prepends the inverse-transform image to the main neural network for a joint training. Remarkably, such a simple add-on layer, capable of optimizing the frequency content of an input for a specific task, dramatically improves the performance of the main network regardless of its design. We observe that the light networks gain a noticeable boost in the performance metrics; whereas, the training of the heavy ones converges faster when GAFL is prepended to the main architecture. We showcase the performance of the layer in four classical computer vision tasks: classification, segmentation, denoising, and erasing, considering popular natural and medical data benchmarks.
keywords: Adaptive neural layer | Efficient training | Fourier filtering
مقاله انگلیسی
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